Autonomous Tracking of Honey Bee Behaviors over Long-term Periods with Cooperating Robots

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376345" target="_blank" >RIV/68407700:21230/24:00376345 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1126/scirobotics.adn6848" target="_blank" >https://doi.org/10.1126/scirobotics.adn6848</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1126/scirobotics.adn6848" target="_blank" >10.1126/scirobotics.adn6848</a>

Result language

angličtina

Original language name

Autonomous Tracking of Honey Bee Behaviors over Long-term Periods with Cooperating Robots

Original language description

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are game-changing technologies in behavioral science. The central element of the most important pollinator species (honeybees) is the colony’s queen. The behavioral strategies of these ecologically important organisms are under-researched, due to the complexity of honeybees’ self-regulation and the difficulties of studying queens in their natural colony context. We created an autonomous robotic observation and behavioral analysis system aimed at 24/7 observation of the queen and her interactions with worker bees and comb cells, generating unique behavioral datasets of unprecedented length and quality. Significant key performance indicators of the queen and her social embedding in the colony were gathered by this tailored but also versatile robotic system. Data collected over 24-hour and 30-day periods demonstrate our system’s capability to extract key performance indicators on different system levels: Microscopic, mesoscopic, and macroscopic data are collected in parallel. Additionally, interactions between various agents are also observed and quantified. Long-term continuous observations yield high amounts of high-quality data when performed by an autonomous robot, going significantly beyond feasibly obtainable results of human observation methods or stationary camera systems. This allows a deep understanding of the innermost mechanisms of honeybees’ swarm-intelligent self-regulation as well as studying other ocial insect colonies, biocoenoses and ecosystems in novel ways. Social insects are keystone species in all ecosystems, thus understanding them better will be valuable to monitor, interpret, protect and even to restore our fragile ecosystems globally.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Science Robotics

ISSN

2470-9476

e-ISSN

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Volume of the periodical

9

Issue of the periodical within the volume

95

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

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UT code for WoS article

001334145900001

EID of the result in the Scopus database

2-s2.0-85206693190